Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification

Shihabudheen, K V; Pillai, G. N.

doi:10.1016/j.knosys.2017.04.007

Cited by 44 publications

(6 citation statements)

References 39 publications

(55 reference statements)

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“…The combination of artificial neural networks (ANNs) with fuzzy logic, called neurofuzzy logic, constitutes ML algorithms used for predicting and identifying critical factors of multifactorial nonlinear systems (Shihabudheen and Pillai, 2017), as it is the case of plant in vitro nutrition (Gallego et al, 2011). Advantages of ANNs algorithms over traditional statistics have been pointed out previously (Landin et al, 2009;Gago et al, 2010a,c).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

et al. 2020

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Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable “IF-THEN” rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

et al. 2020

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“…During the experiment, the rotor speed, DC-link voltage, electromagnetic torque, real power flow, and reactive power flow were monitored under steady-state conditions and during symmetrical and asymmetrical voltage dips. In [29], an Extreme Learning Adaptive Neuro-Fuzzy Inference System is proposed for the Doubly Fed Induction Generator (DFIG) Wind Energy Conversion System (WECS) to overcome contingencies and source-side disturbances such as wind speed and grid-side disturbances. In [30], a single input variable fuzzy logic controller is designed for the DFIG WECS, which performs harmonic compensation, mitigates unbalanced load currents, and maximizes power extraction using the tip speed ratio technique, thus improving the dynamic response of the system under small perturbations.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Low Voltage Ride-Through Resilience in Grid-Connected Doubly-Fed-Induction-Generator with Sliding Mode Controller

Ananth,

Vineela

2024

Preprint

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In power systems, grid disturbances are prevalent, causing significant challenges for Doubly-Fed Induction Generators (DFIGs). During these disturbances, the windings of DFIGs are exposed to large offset inrush currents, which can damage the converter switches and capacitors. Additionally, low voltage faults result in a dip in the rotor speed, leading to current injection in the rotor windings at the changed rotor slip frequency during the fault. This behaviour can be detected using stator two-axis instantaneous voltage and flux. In this study, we propose an innovative application of Sliding Mode Control (SMC) in both the rotor-side converter (RSC) and grid-side converter (GSC) control schemes of a DFIG. The primary objective is to effectively control the flux decay and reduce torque pulsations, rotor speed variations, and current deviations through an enhanced SMC approach. The proposed technique compensates and rapidly decomposes the stator dc-offset flux component based on deviations in the speed of the rotor with the reference value and the dc flux. To assess the effectiveness of the proposed approach, we conduct a comparative analysis between the conventional Proportional-Integral (PI) control and the proposed SMC technique under symmetrical grid fault conditions. The results demonstrate the superior performance of the enhanced SMC in mitigating the adverse effects of grid disturbances on DFIGs.

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“…In the generalized Bernstein fuzzy system, the premise parameters are randomly generated and the consequent parameters are determined by solving a linear optimization problem. In [15], a regularized extreme learning adaptive neuro-fuzzy inference system (R-ELANFIS) is designed to improve the generalization performance of a neuro-fuzzy system. In [16], a broad learning system (BLS) is proposed for fast learning of a large volume of data, which randomly assign the values of input parameters and expand the neurons in a broad manner without a retraining process.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

Zhou

Bao

2023

Mathematics

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In this paper, a stochastic configuration based fuzzy inference system with interpretable fuzzy rules (SCFS-IFRs) is proposed to improve the interpretability and performance of the fuzzy inference system and determine autonomously an appropriate model structure. The proposed SCFS-IFR first accomplishes a fuzzy system through interpretable linguistic fuzzy rules (ILFRs), which endows the system with clear semantic interpretability. Meanwhile, using an incremental learning method based on stochastic configuration, the appropriate architecture of the system is determined by incremental generation of ILFRs under a supervision mechanism. In addition, the particle swarm optimization (PSO) algorithm, an intelligence search technique, is used in the incremental learning process of ILFRs to obtain better random parameters and improve approximation accuracy. The performance of SCFS-IFRs is verified by regression and classification benchmark datasets. Regression experiments show that the proposed SCFS-IFRs perform best on 10 of the 20 data sets, statistically significantly outperforming the other eight state-of-the-art algorithms. Classification experiments show that, compared with the other six fuzzy classifiers, SCFS-IFRs achieve higher classification accuracy and better interpretation with fewer rules.

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Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification

Cited by 44 publications

References 39 publications

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

Enhancing Low Voltage Ride-Through Resilience in Grid-Connected Doubly-Fed-Induction-Generator with Sliding Mode Controller

Stochastic Configuration Based Fuzzy Inference System with Interpretable Fuzzy Rules and Intelligence Search Process

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